Download Periodontal infection, impaired fasting glucose and impaired

J Clin Periodontol 2014; 41: 643–652 doi: 10.1111/jcpe.12258
Periodontal infection, impaired
fasting glucose and impaired
glucose tolerance: results from
the Continuous National Health
and Nutrition Examination
Survey 2009–2010
Arora N, Papapanou PN, Rosenbaum M, Jacobs DR Jr, Desvarieux M, Demmer
RT. Periodontal infection, impaired fasting glucose and impaired glucose tolerance:
results from The Continuous National Health and Nutrition Examination Survey
2009–2010. J Clin Periodontol 2014; 41: 643–652. doi: 10.1111/jcpe.12258
Abstract
Aim: We investigated the relationship between periodontal disease, a clinical
manifestation of periodontal infection, and pre-diabetes.
Methods: The National Health and Nutrition Examination Survey, 2009–2010
enrolled 1165 diabetes-free adults (51% female) aged 30–80 years
(mean SD=50 14) who received a full-mouth periodontal examination and an
oral glucose tolerance test. Participants were classified as having none/mild, moderate or severe periodontitis and also according to mean probing depth ≥2.19 mm or
attachment loss ≥1.78 mm, (respective 75th percentiles). Pre-diabetes was defined
according to ADA criteria as either: (i) impaired fasting glucose (IFG) or impaired
glucose tolerance (IGT). In multivariable logistic regression models, the odds of
IFG and IGT were regressed on levels of periodontitis category.
Results: The odds ratios and 95% confidence intervals for having IGT among participants with moderate or severe periodontitis, relative to participants with none/mild periodontitis were 1.07 [0.50, 2.25] and 1.93 [1.18, 3.17], p = 0.02. The ORs for having IFG
were 1.14 [0.74, 1.77] and 1.12 [0.58, 2.18], p = 0.84. PD ≥75th percentile was related to
a 105% increase in the odds of IGT: OR [95% CI] = 2.05 [1.24, 3.39], p = 0.005.
Conclusions: Periodontal infection was positively associated with prevalent impaired
glucose tolerance in a cross-sectional study among a nationally representative sample.
Nidhi Arora1,†, Panos N. Papapanou2,
Michael Rosenbaum3, David R.
Jacobs Jr4,5, Mo€ıse Desvarieux1,6 and
Ryan T. Demmer1,†
1
Department of Epidemiology, Mailman
School of Public Health, Columbia University,
New York, NY, USA; 2Division of
Periodontics, Section of Oral and Diagnostic
Sciences, College of Dental Medicine,
Columbia University, New York, NY, USA;
3
Division of Molecular Genetics, Departments
of Pediatrics and Medicine, Columbia
University, New York, NY, USA; 4Division of
Epidemiology and Community Health, School
of Public Health, University of Minnesota,
Minneapolis, MN, USA; 5Department of
Nutrition, University of Oslo, Oslo, Norway;
6
miologies et
Centre de recherche Epide
Biostatistique, INSERM U1153 Equipe:
thodes en e
valuation the
rapeutique des
Me
maladies chroniques, Paris, France
†
Contributed equally to this manuscript.
Key words: glucose metabolism; infection;
periodontal disease; periodontitis
Accepted for publication 1 April 2014
Conflict of interest and source of funding statement
The authors declare that they have no conflict of interests. This research was supported by NIH grants R00 DE-018739 and R21
DE-022422 to Dr. Demmer. Additional funding support was provided by a Pilot & Feasibility Award to Dr. Demmer from the
Diabetes and Endocrinology Research Center, College of Physicians and Surgeons, Columbia University (DK-63608); Dr. Des
varieux also receives support from R01 DE-13094 and a Chair in Chronic Disease, Ecole
des Hautes Etudes
en Sante Publique,
France.
© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
643
644
Arora et al.
There is evidence that chronic infections might increase the risk for diabetogenesis. For example, clinical
indicators of periodontal infection
were reported to be associated with
a twofold increase in the risk of diabetes development during 20 years
of prospective follow-up (Demmer
et al. 2008) and more recently, serological evidence of H. pylori infection was found to be associated with
a 2.7-fold increase in risk for incident diabetes (Jeon et al. 2012).
Studies have also examined the
relationship between infection and
early markers of impaired glucose
metabolism to advance our understanding of the natural history of
associations. Most research on this
topic arises from designs using periodontal infection models to study
the general hypothesis of microbialinduced diabetes risk. Periodontal
infection models are useful because
minimally invasive clinical measures
are manifestations of adverse subgingival microbial exposures (Demmer
et al. 2010b). Accordingly, clinical
periodontal measures have been
reported to be associated with
increased risk for accelerated 5-year
progression of haemoglobin A1c
(A1c; Demmer et al. 2010a) as well
as elevated levels of insulin and insulin resistance (Demmer et al. 2012b).
Periodontal infections have also
been linked to increased risk for prediabetes defined as either impaired
fasting glucose (Zadik et al. 2010,
Choi et al. 2011) or impaired glucose
tolerance (Saito et al. 2004). The initial reports linking periodontal infections to pre-diabetes have provided
helpful insights but some important
limitations exist such as: (i) lack of
full-mouth
clinical
periodontal
exams (Saito et al. 2004, Zadik et al.
2010, Choi et al. 2011) that can
more accurately reflect the extent
and severity of infection; (ii) exclusion of women (Saito et al. 2004,
Zadik et al. 2010); and/or (iii) the
use of old criteria for defining prediabetes (Saito et al. 2004). Moreover, no previous study has provided
results comparing the relative
strength of association between
infection and both impaired fasting
glucose (IFG) and impaired glucose
tolerance (IGT) in separate analyses
from the same study population.
Comparative studies of these outcomes would be meaningful as IFG
and IGT are believed to each portend different levels of risk for future
diabetes and cardiovascular disease.
IFG and IGT might also represent
a different underlying pathophysiology and diabetes risk phenotype
(Blake et al. 2004, Nathan et al.
2007).
We studied the association between
clinical measures of periodontal infection and pre-diabetes. Periodontal
infections were assessed using fullmouth periodontal examinations and
pre-diabetes was defined using both
fasting glucose and 2-h post-challenge
glucose levels. Participants were adult
men and women enrolled in the
Continuous NHANES 2009–2010, a
randomly sampled, population-based
study of non-institutionalized US residents.
Methods
The Continuous National Health and
Nutrition
Examination
Survey
(NHANES) 2009–2010 is a nationally
representative, stratified, multistage
probability sample of the civilian
non-institutionalized US population.
The current analysis includes men
and women aged 30–80 years of age
who received a periodontal examination and an oral glucose tolerance test
(OGTT). Individuals were excluded if
they had diabetes as determined via:
(i) Self-reported, diabetes diagnosis;
or (ii) HbA1c levels ≥6.5% or (iii)
fasting glucose ≥126 mg/dl. Individuals were also excluded if they: (i) were
not fasting for ≥9 h at the time of the
first OGTT blood collection; or (ii)
were missing important covariate
data collection. The final sample size
for the current analysis is n = 1165.
Periodontal examination
Periodontal probing depth (PD) and
clinical attachment loss (AL) measurements were made by trained,
registered hygienists in the full-mouth
(excluding third molars) at six sites
per tooth (Eke et al. 2012). Periodontal examiners received intense
training followed by periodic monitoring and recalibration against a
reference examiner. The reference
examiner made three visits to each
dental examination team per year
to observe field operations and to
replicate 20–25 oral health examinations.
Oral glucose tolerance test
Plasma was collected after a minimum 9-h fast. Immediately after the
initial venipuncture, participants
were then asked to drink a calibrated 75 gram dose of glucose solution (TrutolTM). A second venous
plasma collection was performed 2 h
after the glucose challenge (http://
www.cdc.gov/nchs/data/nhanes/nhanes
_09_10/OGTT.pdf). Plasma specimens
were processed, stored and shipped to
Fairview Medical Center Laboratory
at the University of Minnesota, Minneapolis, Minnesota for analysis. Glucose concentration was determined by
a hexokinase method (Demmer et al.
2012b).
Pre-diabetes definitions
Impaired fasting glucose (IFG) and
Impaired glucose tolerance (IGT) were
defined in accordance with the American Diabetes Association (ADA) criteria (2012) as follows: IFG = fasting
plasma glucose ≥100 mg/dl and
<126 mg/dl; IGT = 2-h post-challenge
glucose values ≥140 mg/dl and
<200 mg/dl.
Risk factor assessment
Comprehensive questionnaires to
assess risk factors relevant to both
periodontal disease and pre-diabetes
were administered as previously
described (Demmer et al. 2012b).
The demographic variables age, race/
ethnicity, sex, education (<high
school, completed high school, some
college, college graduate) and poverty-income-ratio
(calculated
by
dividing family income by the poverty guidelines, specific to family size,
as well as the appropriate year and
state according to Department of
Health and Human Services guidelines) were collected. Behavioural risk
factor assessments included physical
activity level (none, moderate and
vigorous based on occupational and
recreational related physical activity
performed in a typical week), cigarette smoking, alcohol consumption
and caloric intake. Height, weight
and blood pressure measures were
made by trained research assistants
according to standardized protocols.
BMI was calculated as weight (kilograms)/height (meters)2 and participants
were
categorized
as
underweight/normal weight (<25
© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
Periodontal infection and pre-diabetes
kg/m2), overweight (25–29.9 kg/m2)
or obese (≥30 kg/m2). Triglycerides,
total cholesterol and HDL-cholesterol, C-reactive protein (CRP) and
white blood cell count (WBC) were
measured from fasting blood samples.
Statistical analysis
SAS Survey procedures (version 9.3)
were used for data analysis. Analysis
of variance and categorical analysis
methods were used to obtain
descriptive statistics according to
both periodontal and pre-diabetes
status for important demographic,
behavioural, dental and cardiometabolic variables. p-Values presented
arise from F-statistics or chi-square
statistics. The odds of pre-diabetes
defined as any-IFG (irrespective of
IGT) or any-IGT (irrespective of
IFG) were regressed across categories of periodontal disease in multivariable logistic regression models.
Periodontal infection was defined
using three separate approaches
based on measures of PD and AL.
First, participants were categorized
as having non/mild, moderate or
severe periodontitis according to the
Centers for Disease Control and Prevention/American Academy of Periodontology (CDC/AAP) definition
(Page & Eke 2007). A second definition was created by categorizing participants as being either ≥75th
percentile or <75th percentile for
mean PD at inter-proximal sites
(2.19 mm). The third periodontal
definition was based on being either
≥75th percentile or <75th percentile
for mean AL at inter-proximal sites
(1.78 mm). For the latter two definitions, supplemental analyses were
conducted using cut-points derived
from all periodotnal sites (i.e. including mid-facial and mid-buccal sites).
For regressions modelling, the CDC/
AAP definition of periodontitis as
the primary exposure, we additionally report the p-value for linear
trend derived from the ordinal threelevel periodontitis variable (none/
mild, moderate or severe).
In addition, multivariable generalized logistic regression models examined the association between CDC/
AAP defined periodontitis and a
polytomous pre-diabetes outcome
which categorized participants as
having either: (i) no pre-diabetes; (ii)
isolated-IFG; (iii) isolated-IGT or (iv)
combined IFG & IGT. NHANES
survey weights, cluster and strata
variables were included in the analysis
to account for the complex survey
design as previously described (Demmer et al. 2012b). In addition to odds
ratios and 95% confidence intervals,
we report the p-value for any difference in the odds of the polytomous
pre-diabetes outcome according to
level of periodontitis derived from
Wald chi-square tests.
A series of multivariable models
were developed to better assess the
influence of confounding. On the
basis of data availability, we focus on
confounding by sociodemographic
indicators (age, sex, race/ethnicity
and educational level), health behaviours (smoking status, caloric intake,
alcohol consumption and physical
activity) and adiposity (body mass
index). We also consider variables
that might mediate the association
between periodontal infection and
pre-diabetes (blood pressure, cholesterol profile, WBC and CRP). Models
were additionally informed by two
Directed Acyclic Graphs (DAGs)
constructed using, Dagitty (Textor
et al. 2011). Figure 1A assumes more
complex causal structures in which
potential sociodemographic confounders relate to periodontal infection and pre-diabetes through
multiple mechanisms (e.g. confounding can act through health behaviours
and adiposity but also through other
mechanisms not represented in our
data); this causal structure necessitates adjustment for all sociodemographic variables. Alternatively, the
DAG in Figure 1B assumes that all
confounding effects act through
either health behaviours or adiposity;
this causal structure does not require
adjustment for sociodemographic
variables if health behaviours and
adiposity adjustments are made.
The following multivariable models were considered. Model 2 adjusted
for only health behaviours (smoking
status, caloric intake, alcohol consumption and physical activity) and a
marker of adiposity (body mass
index) based on assumptions inherent
in Figure 1B. Model 3 adjusted for
only the sociodemographic variables
age, sex, race/ethnicity and educational level. Model 4 adjusted for
health behaviours and sociodemographic variables. Model 5 adjusted
© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
645
for sociodemographics, health behaviours and adiposity. Finally, model 6
expanded model 5 by additionally
adjusting for variables that could be
considered as either a confounder or
mediator of associations between
periodontal infection and pre-diabetes depending on the causal structure
hypothesized; this model included
adjustment for systolic blood pressure, total cholesterol-to-HDL ratio,
WBC and CRP as previous reports
suggest periodontal infection as a
possible risk factor for these outcomes (D’Aiuto et al. 2006, Desvarieux et al. 2010, Demmer et al.
2013). Unless otherwise stated, ORs
reported in the main text were derived
from model 5 as we believe this provides the best combination of parsimony and validity.
To provide additional information regarding the magnitude and
direction of confounding by the
aforementioned variables considered
individually, we also constructed
tables summarizing the difference
between parameter estimates from
logistic models with more versus
less covariable adjustment using a
“change-in-estimate”
approach
(Mickey & Greenland 1989); changein-estimate
was
defined
as
follows: [(LN (more adjusted OR)
LN (less adjusted OR))/LN (adjusted
OR)] 9 100, yielding the percent
change in the OR resulting from
lack of adjustment. The approach
uses 13 model selection iterations
(the number of possible covariables).
Iteration 1 started with the unadjusted parameter estimate for periodontal infection and then ran 13
separate regressions considering the
influence of all potential confounders on the unadjusted parameter
estimate. The confounder that
produced the greatest change-in-estimate was added to the regression to
form an “intermediate” model and
another modelling iteration was
repeated; each modelling iteration
increases the number of independent variables in the intermediate
model by 1 and decreases the number
of remaining confounders by 1. Four
variables hypothesized as possible
mediators were assessed in the
last four interactions. Results from
our change-in-estimate analysis did
not suggest that the aforementioned models 1–6 would be inappropriate.
646
Arora et al.
PPrediabetes
di b t
I f i
Infection
Mediators
Hypertension,
Hypercholesteremia,
Inflammation
Adiposity
Health Behaviors
Diet,
DietPhysical
PhysicalAActivity,
ctivity
Smoking, Alcohol
Socio
Socio demographic
Variables
Age, Sex, Race, Education
(A)
Infection
Prediabetes
Mediators
Hypertension,
Hypercholesteremia,
Inflammation
Adiposity
Health Behaviors
Diet, Physical Activity,
Smoking, Alcohol
(B)
Socio demographic
demographic
Socio
Variables
Age, Sex, Race, Education
Fig. 1. (A) Directed Acyclic Graph (DAG) representing one possible underlying causal
structure of inter-relationships among periodontal infection, pre-diabetes and several
mediators or confounders of the association. This causal structure assumes potential
sociodemographic confounders relate to periodontal infection and pre-diabetes
through multiple mechanisms (e.g. confounding by sociodemographic variables can act
through health behaviours and adiposity but also through other mechanisms not represented in our data); this causal structure would necessitate adjustment for all
sociodemographic variables. A similar argument can be made for more proximal variables in the causal chain such as health behaviours and adiposity. Only statistical
adjustment for all potential confounders will provide the least confounded estimate.
(B) Directed Acyclic Graph (DAG) representing one possible underlying causal structure of inter-relationships among periodontal infection, pre-diabetes and several mediators or confounders of the association. This causal structure assumes that all
confounding effects of sociodemographic variables act entirely through effects on
either health behaviours or adiposity (two constructs which are measured in our data);
this causal structure does not require adjustment for sociodemographic variables so
long as health behaviours and adiposity adjustments are made.
Results
General characteristics
Participants had a mean SD age of
50 14 years and 51% were women.
Hispanics, Whites and Blacks represented 31%, 49% and 15% of the
sample (5% reported other race/ethnicity). Mean PD was (mean SD)
1.63 0.58 mm and mean AL was
1.59 1.05 mm. The prevalence of
moderate and severe periodontitis
was 33% and 10%, respectively, and
the remaining participants had no/
mild periodontitis. Periodontitis was
associated with adverse levels of several risk factors for cardiometabolic
disease such as age, education smoking status, HDL-cholesterol and systolic blood pressure (Table S1). Body
mass index and obesity prevalence
did not vary according to levels of
periodontitis (Table S1). Table S1
also summarizes periodontal clinical
characteristics across periodontitis
status to better inform the severity of
disease in this population-based sample. For example, mean PD and AL
among individuals with severe periodontitis were 2.5 mm and 3.4 mm
respectively. In comparison, mean
PD and AL values among individuals
with PD or AL ≥75th percentile were
2.4 mm and 2.9 mm respectively.
Variation in levels of cardiometabolic risk factors across levels of
mean AL or PD were similar to what
was observed for periodontitis. However, participants with mean AL
≥75th versus <75th percentile were
7 years older on average (p < 0.0001)
while those with PD ≥75th versus
<75th percentile were only 1 year
older (p = 0.18) which is consistent
with previous report from NHANES
(Demmer et al. 2012b).
The mean SD values for 2-h
post-challenge glucose and fasting
plasma glucose were 109 32 mg/dl
and 98 9 mg/dl (Table S1). The
respective prevalence estimates for
isolated-IFG, isolated-IGT and combined IFG+IGT were 33%, 6% and
10%; the prevalence estimates of any
IFG and any IGT were 43% and
16%. Among participants with versus without IGT, 65% and 39% also
had IFG (26% risk difference,
p < 0.0001). In comparison, among
participants with versus without
IFG, 25% versus 10% also had IGT
(15% risk difference, p < 0.0001).
As compared to participants without pre-diabetes or with isolated-IFG,
participants with isolated-IGT tended
to be older, female, have higher CRP
and WBC levels but intermediate fasting insulin and HOMA-IR values;
they were also less likely to smoke
or participate in vigorous activity
(Table 1). Fasting glucose levels were
similar among participants without
any pre-diabetes and those with isolated-IGT (1.5 mg/dl difference,
© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
Periodontal infection and pre-diabetes
p = 0.11). In contrast, 2-h post-challenge glucose levels were higher in isolated-IFG versus no pre-diabetes:
105 1.3 versus 93 0.9, p < 0.0001
(Table 1). Relative to participants
without any IGT/IFG, or those with
647
isolated-IFG, those with isolated-IGT
were older, had lower indicators of
socioeconomic status and higher lev-
Table 1. Characteristics of participants according to pre-diabetes status. One thousand one hundred and sixty five men and women 30–
80 years old, enrolled in The Continuous National Health and Nutrition Examination Survey (NHANES) 2009–2010
No Pre-diabetes,
n = 598 (51%)
Age (years)
46.9 0.6
Male (%)
38
Race/ethnicity
% Hispanic
30
% Non-hispanic white
49
% Non-hispanic black
16
% Other
5
Education
% Less than high school
22
% Completed high school
21
% Some college or AA degree 29
% College graduate and above 29
Family poverty income ratio
3.3 0.1
Smoking status
% Never
60
% Former
23
% Current
17
CDC BMI category, %
36
<25 kg/m2
37
25–29.9 kg/m2
27
≥30 kg/m2
27.3 0.5
BMI (kg/m2)
Alcohol use (grams/day)
10.9 1.4
Physical activity in a typical week, %
None
28
Moderate activity
35
Vigorous activity
37
Kilocalories consumed
2118.9 45
in previous 24 h
Periodontal status
Mean probing depth (mm)
1.47 0.03
Mean attachment loss (mm)
1.31 0.04
Periodontal disease (CDC AAP definition), %
Healthy
64
Moderate
29
Severe
7
Blood pressure
Systolic BP (mmHg)
116 0.9
Diastolic BP (mmHg)
68 1.0
HDL-cholesterol
58 0.6
LDL-cholesterol
122 0.8
Total cholesterol-to3.7 0.04
HDL-cholesterol ratio
6.2 0.09
WBC count (cells 9 109/l)
hs-C-reactive Protein (mg/l)
2.6 0.2
American Heart Association hs-CRP Categories, %
<1.0 mg/l
36
1.0–3.0 mg/l
36
>3.0 mg/l
28
Plasma fasting glucose (mg/dl)
91.7 0.3
Two hour glucose
93 0.9
(OGTT) (mg/dl)
Insulin levels (lU/ml)
9.8 0.4
HOMA-IR
2.2 0.1
5.3 0.01 (34 0.1)
HbA1c% (mmol/mol)
a
Isolated IFG,
n = 379 (33%)
Isolated IGT,
n = 66 (6%)
Combined
IFG & IGT,
n = 122 (10%)
p-value
48.9 0.8a
66a
55.2 2.7a,b
26b
57.3 1.5
57
31
49
14
6
32
47
15
6
31
50
13
6
0.99
26a
25
26
24
3.3 0.1
36a
20
27
17
2.9 0.2a,b
31
25
26
17
3.1 0.2
0.004
53
26
21
68b
18
14
54
29
17
0.09
18a
41
42
29.9 0.3a
10.0 1.33
21a
38
41
29.8 1.1a
4.9 2.8
20
32
48
30.9 0.8
9.9 2.4
26
33
41
2419.8 81a
44a,b
35
21
1957.3 132b
41
34
25
2120.7 94
0.004
1.60 0.04a
1.51 0.08a
1.56 0.09
1.46 0.12
1.68 0.09
1.90 0.14
0.004
0.003
53a
36
11
52
38
11
40
37
23
<0.0001
121 0.8a
71 1.0a
50 1.0a
124 2.8
4.3 0.08a
125 1.8a
70 1.8
56 3.2
127 3.9
4.1 0.2
124 1.9
70 1.6
52 2.0
117 3.1
4.1 0.2
<0.0001
0.1
<0.0001
0.25
<0.0001
6.5 0.13
4.4 0.6a
6.7 0.26
5.4 0.7a
6.9 0.2
5.0 0.8
0.01
<0.0001
30a
36
34
105.8 0.4a
105 1.3a
27a,b
23
50
93.2 1.0b
158 3.2a,b
25
27
48
109.1 0.8
166 1.7
<0.0001
<0.0001
<0.0001
15.3 0.6a
4.0 0.17a
5.5 0.02 (37 0.2)a
12.7 1.1a
2.9 0.3a,b
5.6 0.07 (38 0.8)a
17.4 1.4
4.7 0.4
5.7 0.05 (39 0.5)
<0.0001
<0.0001
<0.0001
p < 0.05 for any comparison with the no pre-diabetes group.
p < 0.05 for comparisons between IFG and IGT.
b
© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
<0.0001
<0.0001
0.11
<0.0001
0.0004
0.28
0.04
648
Arora et al.
els of inflammatory biomarkers, but
were less likely to be ever smokers
Table 1.
Periodontal status and pre-diabetes
After multivariable adjustment, including smoking status and BMI, the
odds ratios for any-IGT among participants with moderate or severe
periodontitis, relative to those with
no/mild periodontitis, were 1.07 [0.50,
2.25] and 1.93 [1.18, 3.17] (Table 2,
model 5). In contrast, the ORs [95%
CI] for any-IFG among participants
with moderate or severe periodontitis,
respectively, were 1.14 [0.74, 1.77] and
1.12 [0.58, 2.18] (Table 2, model 5).
The odds for any-IGT were
increased by 105% when comparing
4th quartile versus 1st–3rd quartiles of
mean PD: OR [95% CI]=2.05 [1.24,
3.39] (Table 3, model 5). The ORs
summarizing the relationship between
mean AL and any-IGT were attenuated as were results for any-IFG
(Table 3). Findings were similar when
modelling PD and AL continuously
(Table S2) and also when PD and AL
were derived from all periodontal
sites, including mid-facial and midbuccal sites (Table S3). Additional
adjustment for potential mediators
did not meaningfully change observed
ORs or the interpretation of results
(Tables 2, 3, model 6).
Results from change-in-estimate
analyses to quantify the magnitude
and direction of confounding revealed
important patterns (Tables S4–S6).
For the probing depth exposure, education level, smoking status, race/ethnicity, age and activity level were the
strongest confounders. For the AL
exposure age, smoking status and
education adjustments produced
meaningful changes in the odds ratio;
the age-related confounding was substantial. Confounding patterns for the
CDC/AAP defined periodontitis
exposure were similar to those for
mean AL. In all analyses smoking status, race/ethnicity and activity level
demonstrated patterns of negative
confounding in which adjustment for
these variables strengthened, rather
than attenuated, the association
between periodontal infection and
pre-diabetes. Negative confounding
was also observed for sex adjustments
although the magnitude of sex-related
confounding was minimal (Tables
S4–S6).
Table 2. Odds ratios (95% CI) for prevalent pre-diabetes according to periodontal status.
One thousand one hundred and sixty five men and women 30–80 years old, enrolled in The
Continuous National Health and Nutrition Examination Survey (NHANES) 2009–2010
Healthy/Mild
periodontitis
(n = 665)
Moderate
Periodontitis
(n = 383)
Severe
Periodontitis
(n = 117)
Pre-diabetes outcome defined as any impaired glucose tolerance
IGT prevalence
12%
18%
Model 1
Ref.
1.70 (0.88–3.3)
2.56
Model 2
Ref.
1.67 (0.85–3.28)
2.90
Model 3
Ref.
1.04 (0.52–2.06)
1.75
Model 4
Ref.
1.04 (0.49–2.20)
2.01
Model 5
Ref.
1.07 (0.50–2.25)
1.93
Model 6
Ref.
1.04 (0.51–2.11)
1.87
Pre-diabetes outcome defined as any impaired fasting glucose
IFG prevalence
38%
48%
Model 1
Ref.
1.54 (1.12–2.14)
2.01
Model 2
Ref.
1.62 (1.11–2.37)
1.99
Model 3
Ref.
1.14 (0.79–1.65)
1.14
Model 4
Ref.
1.11 (0.73–1.69)
1.15
Model 5
Ref.
1.14 (0.74–1.77)
1.12
Model 6
Ref.
1.08 (0.70–1.68)
1.05
p for linear
trend
31%
(1.65–3.98)
(1.80–4.68)
(1.16–2.62)
(1.27–3.20)
(1.18–3.17)
(1.10–3.16)
<0.0001
<0.0001
0.01
<0.01
0.02
0.04
59%
(1.28–3.14)
(1.17–3.38)
(0.68–1.90)
(0.65–2.01)
(0.58–2.18)
(0.56–1.99)
0.004
0.01
0.78
0.86
0.84
0.94
Model 1: Crude; Model 2: smoking, total calorie intake, total alcohol intake, physical activity, BMI; Model 3: age, sex, race/ethnicity, education level, Model 4 model 3+ smoking,
total caloric intake, total alcohol intake, physical activity; Model 5: model 4+ BMI; Model
6: model 5+ systolic blood pressure, total cholesterol/hdl ratio, total WBC count and CRP.
Table 3. Odds ratios for prevalent pre-diabetes according to mean probing depth and mean
attachment loss levels. One thousand one hundred and sixty five men and women 30–80years old, enrolled in The Continuous National Health and Nutrition Examination Survey
(NHANES) 2009–2010
Mean probing depth*
<75th
percentile
(n = 873)
≥75th percentile
(n = 292)
p-value
Mean attachment loss*
<75th
percentile
(n = 874)
p-value
≥75th percentile
(=291)
Pre-diabetes outcome defined as any impaired glucose tolerance (IGT)
IGT
14%
23%
14%
24%
prevalence
Model 1
Ref.
2.06 (1.31–3.21)
0.002
Ref
1.95 (1.24–3.07)
Model 2
Ref.
2.26 (1.43–3.58) <0.001
Ref
2.09 (1.39–3.16)
Model 3
Ref.
1.97 (1.26–3.07)
0.003
Ref
1.31 (0.85–2.01)
Model 4
Ref.
2.23 (1.37–3.62)
0.001
Ref
1.43 (0.91–2.24)
Model 5
Ref.
2.05 (1.24–3.39)
0.005
Ref
1.41 (0.90–2.22)
Model 6
Ref.
1.99 (1.17–3.38)
0.01
Ref
1.32 (0.84–2.09)
Pre-diabetes outcome defined as any impaired fasting glucose (IFG)
IFG
39%
55%
39%
55%
prevalence
Model 1
Ref.
1.62 (1.16–2.27)
0.005
Ref.
1.64 (1.10–2.45)
Model 2
Ref.
1.42 (0.91–2.20)
0.12
Ref.
1.63 (1.04–2.58)
Model 3
Ref.
1.22 (0.80–1.85)
0.35
Ref.
1.10 (0.68–1.78)
Model 4
Ref.
1.15 (0.73–1.81)
0.55
Ref.
1.06 (0.61–1.83)
Model 5
Ref.
1.03 (0.59–1.81)
0.91
Ref.
1.04 (0.59–1.81)
Model 6
Ref.
0.98 (0.55–1.75)
0.94
Ref.
0.96 (0.56–1.63)
0.004
<0.001
0.22
0.12
0.14
0.23
0.02
0.03
0.69
0.84
0.90
0.87
*Mean probing depth and attachment loss values are based on inter-proximal sites from all
teeth present excluding 3rd molars.
Model 1: Crude; Model 2: smoking, total calorie intake, total alcohol intake, physical activity, BMI; Model 3: age, sex, race/ethnicity, education level, Model 4 model 3+ smoking,
total caloric intake, total alcohol intake, physical activity; Model 5: model 4+ BMI; Model
6: model 5+ systolic blood pressure, total cholesterol/hdl ratio, total WBC count and CRP.
When considering the relationship between periodontal status and
either isolated-IFG, isolated-IGT or
combined IFG+IGT in generalized
logistic regression models, the
observed ORs were notably larger
© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
Periodontal infection and pre-diabetes
for the isolated-IGT outcome than
for isolated-IFG (Table 4). After full
multivariable adjustment (as in
Model 6, Tables 2, 3), when comparing individuals with mean PD
≥75th versus <75th percentile, the
respective ORs [95% CI] for isolated-IFG, isolated-IGT or combined IFG+IGT were: 0.91 [0.57,
1.45], 1.85 [0.73, 4.66] and 2.06
[0.91, 4.66] (also shown in Table 4).
Respective ORs [95% CI] when
modelling mean AL ≥75th versus
<75th percentile were as follows:
1.13 [0.70, 1.84], 1.11 [0.57, 2.16],
1.45 [1.01, 2.08]; these results were
adjusted for all variables in Table 4
with the exception of mean PD. The
fact that the OR for mean AL predicting combined IFG+IGT was sta-
tistically significant, whereas the
same OR for mean probing depth
was not – despite a larger OR – is
due to a greater NHANES design
effect for the PD versus the AL
parameter estimate.
Results summarizing the observed
relationships between several other
putative pre-diabetes risk factors
including age, gender, educational
level, alcohol consumption, body
mass index, systolic blood pressure
and cholesterol levels are presented
in Table 4.
Conclusion
We have found clinical measures of
periodontal infection to be associated with the pre-diabetic state.
Table 4. Predictors of prevalent pre-diabetes in multivariable logistic regression models
among 1165 men and women aged 30–80 years, enrolled in The Continuous National
Health and Nutrition Examination Survey (NHANES) 2009–2010
Isolated-IFG
Periodontal status
Mean PD ≥75th percentile
Age (10 year increase)
Male versus female
Race/ethnicity
Hispanic versus white
Black versus white
Other versus white
Education level
College grad versus <HS
Some college versus <HS
HS grade versus <HS
Smoking status
Former versus never
Current versus never
Alcohol consumption
1–3 drinks/day versus none
≥4 drinks/day versus none
Caloric Intake
(500 kcal/day increase)
Physical activity level
Moderate versus none
Vigorous versus none
Body mass index
Overweight versus normal
Obese versus normal
Systolic blood pressure
(10 mm Hg increase)
Total cholesterol-to-HDL
cholesterol ratio
(1 unit increase)
CRP level 1–3 mg/l
>3 mg/l
White blood cell count
(1 9 109 cells increase)
Isolated-IGT
Combined
IFG+IGT
p-value*
0.91 [0.57, 1.45] 1.85 [0.73, 4.66] 2.06 [0.91, 4.66]
1.25 [1.07, 1.46] 1.40 [0.99, 1.98] 2.13 [1.65, 2.75]
2.59 [1.78, 3.77] 0.54 [0.22, 1.28] 2.40 [1.08, 5.30]
0.05
<0.0001
<0.0001
1.13 [0.60, 2.11] 0.73 [0.26, 2.04] 1.08 [0.41, 2.86]
0.95 [0.49, 1.84] 0.71 [0.23, 2.25] 0.69 [0.31, 1.56]
2.23 [0.78, 6.36] 0.91 [0.36, 2.28] 2.12 [0.40, 11.1]
0.57
1.03 [0.57, 1.85] 0.62 [0.13, 2.89] 0.99 [0.54, 1.84]
0.86 [0.47, 1.58] 0.64 [0.27, 1.50] 0.81 [0.47, 1.40]
1.26 [0.69, 2.30] 0.70 [0.17, 2.88] 1.33 [0.74, 2.39]
0.001
1.02 [0.66, 1.58] 0.75 [0.32, 1.79] 0.71 [0.35, 1.42]
0.95 [0.55, 1.64] 0.26 [0.09, 0.75] 0.71 [0.32, 1.59]
0.12
0.81 [0.62, 1.05] 0.89 [0.39, 2.06] 0.69 [0.31, 1.54]
0.67 [0.32, 1.40] 0.15 [0.01, 1.67] 1.35 [0.51, 3.62]
1.10 [0.97, 1.25] 1.05 [0.84, 1.31] 1.01 [0.81, 1.27]
0.01
0.43
1.20 [0.76, 1.88] 0.68 [0.29, 1.59] 0.69 [0.41, 1.58]
1.19 [0.65, 2.19] 0.44 [0.14, 1.34] 0.63 [0.36, 1.11]
0.10
1.76 [1.16, 2.68] 1.18 [0.50, 2.80] 2.12 [1.26, 3.58]
2.29 [1.17, 4.48] 0.92 [0.33, 2.58] 3.56 [2.23, 5.70]
1.12 [1.00, 1.24] 1.22 [1.03, 1.45] 1.05 [0.85, 1.32]
<0.0001
1.15 [1.05, 1.26] 1.27 [1.07, 1.51] 1.12 [0.87, 1.45]
0.002
0.78 [0.59, 1.03] 0.82 [0.29, 2.33] 0.63 [0.26, 1.15]
1.41 [0.88, 2.25] 1.91 [0.97, 3.76] 1.61 [0.79, 3.26]
1.07 [0.94, 1.21] 1.12 [0.88, 1.42] 1.11 [0.88, 1.41]
<0.001
0.04
0.78
*p-Value derived from Wald chi-square values used to test the null hypothesis of no difference in the odds of pre-diabetes across levels of exposure.
IFG, Impaired fasting glucose; IGT, Impaired glucose tolerance; PD, Probing Depth.
© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
649
Severe periodontitis was associated
with a 93% increase in the odds of
impaired glucose tolerance after multivariable adjustment. Findings were
similar for mean PD. In contrast,
associations between measures of
periodontal infection and IFG were
weak and not statistically significant.
These findings advance our understanding of the relationship between
diabetes and clinical periodontal
disease. Most research in this area
hypothesizes that associations between
clinical periodontal disease and diabetes status are due to a causal contribution of diabetes to periodontal tissue
destruction (Taylor et al. 1998a,b,
Taylor 2001, Lalla & Papapanou
2011, Demmer et al. 2012a). While
biologically plausible, and supported
by strong findings in several studies
(Taylor 2001, Lalla & Papapanou
2011), the interpretation of results
from most studies has been limited in
a variety of ways, such as by small
sample sizes and/or lack of comprehensive confounder adjustment as
previously discussed (Demmer et al.
2012a). Recent findings arising from a
longitudinal, population-based cohort
with the data collection necessary
to perform comprehensive covariate
adjustments have reported evidence to
support the hypothesis of diabetes as a
causal risk factor for periodontitis,
although findings were much weaker
than previous studies and only uncontrolled diabetes was observed to predict worsening periodontal status
(Demmer et al. 2012a).
It has also been proposed that associations between periodontal infection
and diabetes might be bidirectional.
For example, an impaired immune
response to dysbiotic subgingival biofilms among people with diabetes
might contribute to a chronic inflammatory state and subsequently to both
clinical periodontal disease as well as
heightened insulin resistance and
reduced glycaemic control. The ensuing uncontrolled glycaemia could
further exacerbate periodontal destruction via the formation of advanced
glycation end products (Lalla & Papapanou 2011).
More recently, the hypothesis of
bidirectional relationships between
periodontal infection and diabetes
has been extended to consider
adverse subgingival microbial exposures as a causal risk factor for diabetogenesis (Demmer et al. 2008,
650
Arora et al.
2010a, 2012b). The current findings
bolster this hypothesis by building
on previous research and demonstrating an association between periodontal infection and pre-diabetes.
Although the temporality of associations cannot be established in crosssectional data, it is unlikely that
IFG or IGT could have been a driving causal factor in the development
of severe periodontitis as most previous research suggests that severe
dysglycaemia observed in uncontrolled diabetes is necessary to have
a meaningful influence on periodontal tissue destruction (Taylor et al.
1998a, Tsai et al. 2002, Demmer
et al. 2012a). Nevertheless, it is possible that hyperglycaemia in the prediabetic state might contribute to
compositional shifts in the subgingival microbiome and incipient gingival inflammation.
The hypothesis that infection
might contribute to diabetogenesis is
biologically plausible and fits logically into the larger framework
regarding host inflammatory phenotype as a risk factor for insulin resistance and diabetes development
(Demmer et al. 2008, 2010a, 2012b).
Chronically elevated systemic inflammation has been shown to predict
insulin resistance, (Pradhan et al.
2003, Park et al. 2009) impaired glucose metabolism (Chakarova et al.
2009) and incident T2DM (Pradhan
et al. 2001, Hu et al. 2004). Accordingly, several exogenous inflammatory stimuli such as air pollution
(Kramer et al. 2010), tobacco smoke
(Foy et al. 2005) and pollutants (Lee
& Jacobs 2006, Lee et al. 2007,
2010) have also been linked to
T2DM risk. Regarding the potential
for periodontal infections to trigger
a chronic inflammatory response, it
has been suggested that “keystone”
pathogens such as P. gingivalis
might possess the ability to evade
and/or subvert the host immune system in a manner that enables keystone organisms to persist in the
subgingival space, subsequently shifting the microbial community composition towards a state of dysbiosis
and chronic inflammation (Hajishengallis et al. 2011, 2012). Accordingly,
there is a large body of research
reporting that individuals with clinical evidence of current periodontal
inflammation also have elevated levels of systemic inflammation and
randomized controlled trials have
shown that anti-infective periodontal
treatment can lead to reductions in
systemic inflammation (Lockhart
et al. 2012, Demmer et al. 2013).
In these data, after multivariable
adjustment (excluding potential mediators), the ORs summarizing associations between periodontal infection
and outcomes that included impaired glucose tolerance (i.e. IGT with
or without IFG) ranged from ~1.4–
2.0 depending on the exposure modelled. In contrast, ORs summarizing
associations between infection and
IFG ranged from 0.98–1.05. This suggests that IGT outcomes might be
more relevant vis-
a-vis infection and
potentially supports the mechanistic
involvement of inflammation. Previous studies have demonstrated
increased CRP levels to be more
strongly linked to IGT than IFG outcomes (Muntner et al. 2004, Chakarova et al. 2009, Capaldo et al. 2013)
and our own current results also show
higher CRP levels among participants
with isolated-IGT versus isolatedIFG. Although, we did not observe
strong attenuation of our findings
after adjustment for inflammation,
findings of this nature are not uncommon among studies of periodontal
infection and diabetes risk. As previously discussed, it is possible that
CRP and WBC might be sufficient,
but not necessary mediators in the
causal pathway from microbial exposures to impaired glucose regulation
and a larger set of inflammatory
biomarkers might be required to adequately consider mediation hypotheses (Demmer et al. 2012b). Future
studies with more comprehensive biomarker assessments during longitudinal follow-up will be necessary to
better inform the potential for inflammatory mediation.
ORs were generally larger for
analyses modelling either mean PD
or CDC/AAP periodontitis as the
primary exposure when compared to
mean AL. This is likely due to the
fact that mean AL was notably
lower among participants in the
upper 25th percentile of AL as compared to participants with severe
periodontitis. In contrast, mean PD
was similar among individuals with
severe periodontitis and those in the
upper 25th percentile of PD.
Our apparently null results for
the IFG outcome are in contrast to
positive findings among >12,000 participants in NHANES III (Choi
et al. 2011) in which both AL and
PD were related to an ~20–50%
increase in odds of IFG. However,
the previous NHANES III publication did not measure IGT and it is
possible that the finding was driven
by a higher prevalence of combined
IFG & IGT relative to our current
sample.
Periodontal infection was associated with impaired glucose tolerance
after comprehensive multivariable
adjustment. The strongest confounders in these data appeared to be age,
smoking, race/ethnicity, education
level and activity level. After adjustment for these variables, further
adjustment did not meaningfully
change the strength of association.
Importantly, adjustment for body
mass index – the strongest known
risk factor for pre-diabetes – also
had only marginal influence on the
strength of associations. The fact
that smoking was a negative confounder (i.e. smoking adjustment
strengthened rather than attenuated
results) is notable as smoking has
frequently been suspected as a prominent source of positive confounding
(i.e. smoking adjustment attenuates
results) in studies concerning periodontal infection and non-periodontal outcomes such as cardiovascular
disease and cancer (Hujoel et al.
2002). Therefore, the pattern of negative confounding substantially minimizes the potential for our reported
ORs to be overestimated due to
residual confounding related to
tobacco exposure. The pattern of
negative confounding by smoking
arises from the fact that smoking is
often inversely related to metabolic
outcomes while it is positively
related to periodontal disease; in our
current report, the odds of isolatedIGT were lower among current
smokers relative to never smokers
(see results). Similarly, previous studies found smoking to be a negative
confounder of the relationship
between periodontal infection and
5-year change in haemoglobin A1c
level. That pattern was the result of
inverse associations between baseline
smoking status and longitudinal A1c
change (Demmer et al. 2010a).
We have found clinical indicators
of periodontal infection to be associated with impaired glucose tolerance
© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
Periodontal infection and pre-diabetes
among a nationally representative
sample of US adult men and women.
The exact temporality of associations
cannot be determined in these crosssectional data nor are there sufficient
data to carefully explore the role of
inflammation as an underlying biological mechanism. Longitudinal
studies that collect broader panels of
inflammatory biomarkers will be
important for answering these questions. Nevertheless, the findings are
suggestive of a potential role for
periodontal infections in the aetiology of impaired glucose regulation.
If replicated in future studies, the
public health implications would be
substantial given the high prevalence
of inflammatory periodontal infections in the general population
(Demmer & Papapanou 2010).
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Supporting Information
Additional Supporting Information
may be found in the online version
of this article:
Clinical Relevance
Scientific rationale for the study:
Periodontal infections have been
hypothesized as a potential risk
factor for poor metabolic outcomes,
but limited data are available exploring whether periodontal infections
are differentially associated with
Table S1. General characteristics of
study participants overall and according to periodontal status.
Table S2. Odds ratios for prevalent
pre-diabetes according to continuous
measures of mean probing depth and
mean attachment loss.
Table S3. Odds ratios for prevalent
pre-diabetes according to full-mouth
measures of mean probing depth and
mean attachment loss levels.
Table S4. Evaluation of changes in
odds ratios produced by serial addition of potential confounders to
logistic models examine the relationship between mean probing depth
and impaired glucose tolerance.
Table S5. Evaluation of changes in
odds ratios produced by serial
addition of potential confounders to
logistic models examine the relationship between mean attachment loss
and impaired glucose tolerance.
impaired
glucose
tolerance
or
impaired fasting glucose among diabetes-free individuals.
Principal findings: Periodontitis was
associated with increased odds of
impaired glucose tolerance but not
impaired fasting glucose.
Table S6. Evaluation of changes in
odds ratios produced by serial addition of potential confounders to
logistic models examine the relationship between CDC/AAP defined
periodontitis and impaired glucose
tolerance.
Address:
Ryan T. Demmer
Department of Epidemiology
Columbia University
722 W. 168th St.
New York, NY 10032
USA
E-mail: [email protected]
Practical implications: Future research studies are merited to understand whether periodontal infections
are more strongly associated with
specific glucose metabolism phenotypes and whether the observed associations are causal or confounded.
© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd